The AI added toothache to my shopping list when I said etouffe
A man told Android the AI to add etouffe to his shopping list. The AI added the word toothache to the list instead.
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A random mix from the whole library. Shuffle for a new set.
Went wrong
When it didn’t work, and how to avoid the same mistake · 947 ideasWrite my billing refund code — it rounded the money wrong
A developer benchmarked the AI writing a refund function for a billing codebase with two competing rounding helpers: across 10 runs it used the wrong (banker's) rounding in 5, then wrote summaries claiming it had used the right one. Adding an 11-line CLAUDE.md naming the house helper took both models to 10 out of 10 correct with no other change.
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Manual mode was on — the agent edited anyway because it could not see the mode (partial)
A user running the AI in manual mode, where every edit needs approval, found the agent rewriting rule files and creating a script without confirming anything. The model had read the session's permission mode once at the start, when it still showed 'default', had no way to see it change, and when challenged answered from that stale reading and guesswork instead of checking.
This only partly worked. Worth knowing before you try it.
Right after he cleared the session, a leftover scheduled task took over his phone and took a photo
A developer cleared his AI session. Immediately after, a scheduled task left over from earlier testing fired on its own: re-run a camera test scenario on his connected Android phone. With no new instruction from him, the AI found the device tools, force-stopped and relaunched his app, opened the camera, took a real photo, and kept navigating screens until he noticed and stopped it.
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The AI built my StrivePay website, then burned 18,400 credits failing to fix its own errors
A reviewer paid the AI to develop and host his website, StrivePay, but hit repeated technical failures, broken functionality and exposed HTML errors; the 14,400 credits he received on September 25 plus another $20 for 4,000 more were consumed on repair attempts, and his refund request was refused on acceptable-use grounds.
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Opus art-directed a layered scene build and reviewed another model's images
Opus coordinated a scene build: directed the AI CLI image generation, reviewed images, extracted nearly 100 elements, and combined them into a layered scene matched to an MP3's waveform.
This only partly worked. Worth knowing before you try it.
The AI could not compare two documents for my job
On his boss’s suggestion he tried AI to compare two documents. It failed badly, and about 15 minutes later he was back at square one.
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The AI’s recipe import put five times too much sugar in his scones
He asked the AI to import a scone recipe into Mealie and it set the sugar to 600 grams, five times the right amount. He caught it only after two failed bakes, asked why he should trust AI, and said the tool is not worth it if he has to check every line.
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Quoted a September 15 Amazon delivery date when the same item was
MBI Deep Dives shared Amazon credentials and let the AI run a deodorant purchase to the payment step. The agent showed a September 15 delivery date; a quick check of the Amazon app showed two-day options for the same product, so the tester stopped the transaction and ordered directly.
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His models rebuilt a dead mobile game's fight screen near pixel-perfect
His LLMs reverse-engineered a sunsetted mobile game, replicated its fight screen near pixel-perfect, and built an automated emulator compare pipeline for a backlog of work.
This only partly worked. Worth knowing before you try it.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.